Modeling of multiport data characterizing high-speed modules, such as packages, vias, and complex multiconductor interconnects is becoming increasingly important in signal and power integrity applications. Vector fitting (VF) algorithm has been widely used by designers for macromodeling and system identification from such multiport tabulated data. Since VF and strategies based on it require many iterations to arrive at an optimal number of converged poles, it is highly desired to reduce the computational cost of each VF iteration. This article advances the applicability of VF to exploit the emerging massively parallel graphical processing units (GPUs) by developing necessary parallelization strategies and investigates their performance while using different GPU libraries. For large problem sizes (an increasing number of poles and ports), numerical results demonstrate that the proposed method while using MAGMA libraries provides significant speedup compared with existing multi-CPU-based parallel VF techniques.

中文翻译：

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GVF：基于GPU的矢量拟合，用于多端口列表数据网络建模

在信号和电源完整性应用中，表征高速模块（例如封装，通孔和复杂的多导体互连）的多端口数据建模变得越来越重要。设计人员已广泛使用矢量拟合（VF）算法从此类多端口列表数据进行宏建模和系统识别。由于VF和基于VF的策略需要进行多次迭代才能达到最佳数量的收敛极点，因此非常需要降低每次VF迭代的计算成本。本文通过开发必要的并行化策略，提高了VF利用新兴的大规模并行图形处理单元（GPU）的适用性，并在使用不同的GPU库的同时研究了它们的性能。对于较大的问题（极点和端口数量不断增加），